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Multi-Modal CNN-Ensemble learning with Pansegnet for Early and Accurate Pancreatic Cancer Analysis

Multi-Modal CNN-Ensemble Learning with Pansegnet for Early a...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： Kavitha A Dhanush Sriram R ArunKumar R Department of Information Technology St. Joseph’s College of Engineering Chennai

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Pancreatic cancer is one of the deadliest types of cancer, as it is mostly diagnosed at an advanced stage because no such method to detect this in its preliminary stages exists. This research work now tries to introduce a new method for early and accurate pancreatic cancer analysis by introducing a multimodal CNN-ensemble combined with the latest segmentation techniques, Pansegnet. The proposed system combines histopathological imaging with genomic data to capture both the morphological and genetic variations in pancreatic cancer. This model makes use of the ensemble of CNN architecture through the exploitation of the strong multi-network that improves the accuracy of its prediction. The pansegnet, being a very efficient segmentation model, brings about accurate localization of the tumors which can further be taken into fine-tuning feature extraction toward classification and prediction tasks. Survival is analyzed post-surgery using clinical data through Cox Proportional Hazards and a deep learning-based survival model to predict the outcome of a patient. A multi-modal ensemble-learning framework demonstrates superior performance in the early detection and survival prediction of patients and can be applied to personalized treatment strategies and improved prognosis of patients. The bottom line is that imaging and genomic data need to be combined for a holistic analysis. In developing an integrated model that combines CNN-based feature extraction for high-resolution image analysis, FNN/RNN for processing structured genomic and clinical data, and ensemble learning to enhance reliability in both tumor classification and survival prediction. Additionally, introduced self-adaptive feature fusion mechanism, allowing for detailed multi-scale feature representation and contextual understanding essential for capturing complex tumor morphology and individual genomic variations.

关键词： image segmentation Accuracy Pancreatic cancer Genomics Imaging Predictive models Hazards Bioinformatics Prognostics and health management Tumors

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Remote Sensing-based Socioeconomic Analysis using Task-driven Transfer learning and Regression 5

Remote Sensing-based Socioeconomic Analysis using Task-drive...

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5th IEEE International conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Buddaraju, Sree Teja Bardhan, Ananya Boddu, Ramya Sri Kaur, Simranjit Akilan, Thangarajah Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Lakehead Univ Dept Software Engn Thunder Bay ON Canada

ISBN: (纸本)9781665401791

The economic status of each country varies;some countries are well developed while some are underdeveloped. A lower economic status in any place in the world can lead to hunger, malnutrition, and low life expectancy, especially for children and the older generation. For instance, in Africa, most people live below the international poverty line of 1.25 US dollars per day, according to the World Bank Group. One way of solving this problem is through collecting data and building intelligent models to automatically detect the low economic regions so the organizations, like The United Nations Development Program (UNDP), can allocate vital support systems to save the people there from the severity and help them lead a better life. Unfortunately, obtaining such data through manual surveys takes too long and requires a lot of resources. Thus, this work aims to provide an efficient solution to this problem. It analyzes the socioeconomic status of the underdeveloped regions, primarily a few selected African countries, by using remote sensing (RS), multimodal data exploitation, machine learning, transfer learning, and computer vision technologies. The proposed framework can make accurate prediction on a particular geographic region's standard of living (wealth index) based on the distribution of nightlight intensity that is observed via satellite remote sensing. Exhaustive experiments are carried out using data from the National Oceanic And Atmospheric Administration (NOAA), Demographic and Health Survey (DHS), and Google Static Maps. The experimental results verify that the proposed framework can be used as an effective alternative to the conventional approaches for socioeconomic analysis.

关键词： remote sensing socioeconomic analysis machine learning satellite image processing

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Quantifying the Impact of Complementary Visual and Textual Cues Under image Captioning

Quantifying the Impact of Complementary Visual and Textual C...

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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： Akilan, Thangarajah Thiagarajan, Amitha Venkatesan, Bharathwaaj Thirumeni, Sowmiya Chandrasekaran, Sanjana Gurusamy Lakehead Univ Dept Software Engn Thunder Bay ON Canada Lakehead Univ Dept Comp Sci Thunder Bay ON Canada

ISBN: (纸本)9781728185262

Describing an image with natural sentence without human involvement requires knowledge of both image processing and Natural Language Processing (NLP). Most of the existing works are based on unimodal representations of the visual and textual contents using an Encoder-Decoder (EnDec) Deep Neural Network (DNN), where the input images are encoded using Convolutional Neural Network (CNN) and the caption is generated by a Recurrent Neural Network (RNN). This paper dives into a basic image captioning model to quantify the impact of multimodal representation of the visual and textual cues. The multimodal representation is carried out via an early fusion of encoded visual cues from different CNNs, along with combined textual features from different word embedding techniques. The resultant of the multimodal representation of the visual and textual cues are employed to train a Long Short-Term Memory (LSTM)-based baseline caption generator to quantify the impact of various levels of complementary feature mutations. The ablation study involves two different CNN feature extractors and two types of textual feature extractors, shows that exploitation of the complementary information outperforms the unimodal representations significantly with endurable timing overhead.

关键词： image captioning multimodal learning NLP

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multimodal Dialog for Browsing Large Visual Catalogs using Exploration-exploitation Paradigm in a Joint Embedding Space 19

Multimodal Dialog for Browsing Large Visual Catalogs using E...

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ACM International conference on Multimedia Retrieval (ICMR)

作者： Bhattacharya, Indrani Chowdhury, Arkabandhu Raykar, Vikas C. Rensselaer Polytech Inst Troy NY 12180 USA Rice Univ Houston TX USA IBM Res Bangalore Karnataka India

ISBN: (纸本)9781450367653

We present a multimodal dialog (MMD) system to assist online customers in visually browsing through large catalogs. Visual browsing allows customers to explore products beyond exact search results. We focus on a slightly asymmetric version of a complete MMD system, in that our agent can understand both text and image queries, but responds only in images. We formulate our problem of lshowing the k best images to a userz, based on the dialog context so far, as sampling from a Gaussian Mixture Model (GMM) in a high dimensional joint multimodal embedding space. The joint embedding space is learned by Common Representation learning and embeds both the text and the image queries. Our system remembers the context of the dialog, and uses an exploration-exploitation paradigm to assist in visual browsing. We train and evaluate the system on an MMD dataset that we synthesize from large catalog data. Our experiments and preliminary human evaluation show that the system is capable of learning and displaying relevant products with an average cosine similarity of 0.85 to the ground truth results, and is capable of engaging human users.

关键词： multimodal dialog visual browsing common representation learning interactive AI agent multimodal content analysis and retrieval online human-computer interaction

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Exploration of Deep learning-based multimodal Fusion for Semantic Road Scene Segmentation 14

Exploration of Deep Learning-based Multimodal Fusion for Sem...

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14th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)

作者： Zhang, Yifei Morel, Olivier Blanchon, Marc Seulin, Ralph Rastgoo, Mojdeh Sidibe, Desire Univ Bourgogne Franche Comte ERL VIBOT CNRS 6000 ImViA Lab EA 7535 Le Creusot France

ISBN: (纸本)9789897583544

Deep neural networks have been frequently used for semantic scene understanding in recent years. Effective and robust segmentation in outdoor scene is prerequisite for safe autonomous navigation of autonomous vehicles. In this paper, our aim is to find the best exploitation of different imaging modalities for road scene segmentation, as opposed to using a single RGB modality. We explore deep learning-based early and later fusion pattern for semantic segmentation, and propose a new multi-level feature fusion network. Given a pair of aligned multimodal images, the network can achieve faster convergence and incorporate more contextual information. In particular, we introduce the first-of-its-kind dataset, which contains aligned raw RGB images and polarimetric images, followed by manually labeled ground truth. The use of polarization cameras is a sensory augmentation that can significantly enhance the capabilities of image understanding, for the detection of highly reflective areas such as glasses and water. Experimental results suggest that our proposed multimodal fusion network outperforms unimodal networks and two typical fusion architectures.

关键词： Semantic Segmentation multimodal Fusion Deep learning Road Scenes

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Discovering Connotations as Labels for Weakly Supervised image-Sentence Data 18

Discovering Connotations as Labels for Weakly Supervised Ima...

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27th World Wide Web (WWW) conference

作者： Mogadala, Aditya Kanuparthi, Bhargav Rettinger, Achim Sure-Vetter, York Karlsruhe Inst Technol Karlsruhe Germany BITS Hyderabad India AIFB Karlsruhe Germany

ISBN: (纸本)9781450356404

Growth of multimodal content on the web and social media has generated abundant weakly aligned image-sentence pairs. However, it is hard to interpret them directly due to intrinsic "intension''. In this paper, we aim to annotate such image-sentence pairs with connotations as labels to capture the intrinsic "intension". We achieve it with a connotation multimodal embedding model (CMEM) using a novel loss function. It's unique characteristics over previous models include: (i) the exploitation of multimodal data as opposed to only visual information, (ii) robustness to outlier labels in a multi-label scenario and (iii) works effectively with large-scale weakly supervised data. With extensive quantitative evaluation, we exhibit the effectiveness of CMEM for detection of multiple labels over other state-of-the-art approaches. Also, we show that in addition to annotation of image-sentence pairs with connotation labels, byproduct of our model inherently supports cross-modal retrieval i.e. image query - sentence retrieval.

关键词： image-Sentence Connotation Labels Weakly Supervised Deep learning Multi-label Prediction

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ROBUST COUPLING IN SPACE OF SPARSE CODES FOR MULTI-VIEW RECOGNITION 23

ROBUST COUPLING IN SPACE OF SPARSE CODES FOR MULTI-VIEW RECO...

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23rd IEEE International conference on image Processing (ICIP)

作者： Taalimi, Ali Rahimpour, Alireza Capdevila, Cristian Zhang, Zhifei Qi, Hairong Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781467399616

Classical dictionary learning algorithms that rely on a single source of information have been successfully used for classification tasks. Additionally, the exploitation of multiple sources has shown to be advantageous in challenging real world situations. We propose a new framework to exploit robust modality fusion in classification in order to achieve better classification performance than single source methods. multimodal learning is able to leverage any correlations between sensor modalities found in the data. We propose a new bilevel optimization, referred to as (MCJWDL). We perform supervised dictionary learning while forcing a coupling between the resulting sparse codes from different sources of information. Extensive experiments demonstrate that MCJWDL outperforms state-of-the-art sparse representation and dictionary learning approaches for the multi-view object and multi-view action recognition.

关键词： multimodal classification Robust feature fusion Dictionary learning Coupling

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International Workshop on Medical Computer Vision, MCV 2015 held in conjunction with International Workshop on Medical image Computing for Computer Assisted Intervention, MICCAI 2015

International Workshop on Medical Computer Vision, MCV 2015 ...

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International Workshop on Medical image Computing for Computer Assisted Intervention, 2015 MICCAI

ISBN: (纸本)9783319420158

The proceedings contain 16 papers. The special focus in this conference is on Predicting Disease, Atlas exploitation, Avoidance, Machine learning Based Analyses and Advanced Methods for image Analysis. The topics include: Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly;relationship induced multi-atlas learning for Alzheimer’s disease diagnosis;hierarchical multi-organ segmentation without registration in 3D abdominal CT images;structure specific atlas generation and its application to pancreas segmentation from contrasted abdominal CT volumes;local structure prediction with convolutional neural networks for multimodal brain tumor segmentation;automated segmentation of CBCT image with prior-guided sequential random forest;subject-specific estimation of missing cortical thickness maps in developing infant brains;calibrationless parallel dynamic MRI with joint temporal sparsity;creating a large-scale silver corpus from multiple algorithmic segmentations;PSOAS major muscle segmentation using higher-order shape prior;joint feature-sample selection and robust classification for Parkinson’s disease diagnosis;dynamic tree-based large-deformation image registration for multi-atlas segmentation;hippocampus segmentation from MR infant brain images via boundary regression;a survey of mathematical structures for extending 2D neurogeometry to 3D image processing and efficient 4D non-local tensor total-variation for low-dose CT perfusion deconvolution.

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ROBUST COUPLING IN SPACE OF SPARSE CODES FOR MULTI-VIEW RECOGNITION

ROBUST COUPLING IN SPACE OF SPARSE CODES FOR MULTI-VIEW RECO...

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IEEE International conference on image Processing

作者： Ali Taalimi Alireza Rahimpour Cristian Capdevila Zhifei Zhang Hairong Qi The University of Tennessee Department of Electrical Engineering and Computer Science

ISBN: (纸本)9781467399623

Classical dictionary learning algorithms that rely on a single source of information have been successfully used for classification tasks. Additionally, the exploitation of multiple sources has shown to be advantageous in challenging real-world situations. We propose a new framework to exploit robust modality fusion in classification in order to achieve better classification performance than single source methods. multimodal learning is able to leverage any correlations between sensor modalities found in the data. We propose a new bilevel optimization, referred to as (MCJWDL). We perform supervised dictionary learning while forcing a coupling between the resulting sparse codes from different sources of information. Extensive experiments demonstrate that MCJWDL outperforms state-of-the-art sparse representation and dictionary learning approaches for the multi-view object and multi-view action recognition.

关键词： multimodal classification Robust feature fusion Dictionary learning Coupling Information source coupling Codes Taxonomy Dictionaries as Topic learning indium

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Multisensor image fusion & mining: from neural systems to COTS software

Multisensor image fusion & mining: from neural systems to CO...

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International conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS)

作者： A.M. Waxman D.A. Fay R.T. Ivey N. Bomberger Cognitive Fusion Technology Directorate ALPHATECH Inc. Burlington MA U.S.A.

We summarize our methods for the fusion of multisensor imagery based on concepts derived from neural models of visual processing and pattern learning and recognition. These methods have been applied to real-time fusion of night vision sensors in the field, airborne multispectral and hyperspectral imaging systems, and space-based multiplatform multimodality sensors. The methods enable color fused 3D visualization, as well as interactive exploitation and data mining in the form of human-guided machine learning and search for targets and cultural features. Over the last year we have developed a user-friendly system integrated into a COTS exploitation environment known as ERDAS Imagine. We demonstrate fusion and interactive mining of low-light Visible/SWIR/MWIR/LWIR night imagery, and IKONOS multispectral imagery. We also demonstrate how target learning and search can be enabled over extended operating conditions by allowing training over multiple scenes. This is illustrated for detecting small boats in coastal waters using fused Visible/MWIR/LWIR imagery.

关键词： image fusion Software systems image sensors Hyperspectral sensors Sensor fusion Sensor systems multimodal sensors Pattern recognition image recognition Real time systems

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